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Found 667 Skills
Enables a multi-region AWS CloudTrail trail with S3 log storage, CloudWatch Logs integration, and CloudWatch Logs Insights queries for security monitoring and compliance auditing. Use when setting up centralized API activity logging across all AWS regions.
Configure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
Configure Sentry for error tracking, performance monitoring, and log aggregation. Integrates with Pino to forward logs to Sentry automatically.
Expert in setting up Sentry error tracking and Google Analytics for NestJS and Next.js applications. Use this skill when users need monitoring, error tracking, or analytics configuration.
Implements standardized API error responses with proper status codes, logging, and user-friendly messages. Use when building production APIs, implementing error recovery patterns, or integrating error monitoring services.
Defines database performance monitoring strategy with slow query detection, resource usage alerts, query execution thresholds, and automated alerting. Use for "database monitoring", "performance alerts", "slow queries", or "DB metrics".
Agent Mail inbox monitoring. Check pending messages, HELP_REQUESTs, and recent completions. Triggers: "inbox", "check mail", "any messages", "show inbox", "pending messages", "who needs help".
Implement data quality checks, validation rules, and monitoring. Use when ensuring data quality, validating data pipelines, or implementing data governance.
Watch Linear issues for changes. Use for monitoring updates.
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
Scheduler and background jobs syntax for Frappe/ERPNext v14/v15/v16. Use for scheduler_events in hooks.py, frappe.enqueue() for async jobs, queue configuration, job deduplication, error handling, and monitoring. Triggers on questions about scheduled tasks, background processing, cron jobs, RQ workers, job queues, async tasks.